INFN-SOS

Exercises for INFN School of Statistics

Dependencies and Installation

The jupyter notebooks in this package depend on a few well-known Python packages:

modules	description
jupyterlab	jupyter notebook environment
numpy	array manipulation and numerical analysis
matplotlib	a widely used plotting module for producing high quality plots
scipy	scientific computing
iminuit	Minuit, the celebrated CERN function minimizer
tqdm	progress monitor
imageio	to display images
emcee	Markov chain Monte Carlo sampling
joblib	to save objects to a file and read them back into memory
pandas	data table manipulation, often with data loaded from csv files

Also recommended for symbolic algebra and machine leaning are the following modules:

modules	description
sympy	an excellent symbolic algebra module
pytorch	a powerful, flexible, machine learning toolkit
scikit-learn	easy to use machine learning toolkit

The simplest way to install these packages is first to install miniconda (a slim version of Anaconda) on your laptop by following the instructions at:

https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html

I recommend an installation of miniconda3, which comes pre-packaged with Python 3.

In principle, software release systems such as Anaconda (conda for short) make it possible to have several separate self-consistent named environments on a single machine, say your laptop. For example, you may need to use Python 3.8.x sometimes and Python 3.11.x at other times. If you install software without using environments there is the very real danger that the software on your laptop will become inconsistent. Anaconda (and its lightweight companion miniconda) provide a way to create a consistent software environments But, like all software, Anaconda is far from perfect and problems do sometimes arise!

After installing miniconda3, It is a good idea to update conda before doing anything else using the command

conda update conda

Assuming conda is properly installed and initialized on your laptop, you can create an environment, here we call it sos using the command>

conda create --name sos 

Then activate the desired environment, by doing, for example,

conda activate sos

Now you can install modules into the currently activated environment. For example, you can install jupyter lab (which I suggest you install first) as follows

conda install jupyterlab

Pay attention to the list of python modules that are installed and check if any other the ones recommended above are present. For example, most likely you'll see numpy. If so, you do not need to install this package explicitly. Later, if you wish to update a package, for example jupyterlab, do

conda update jupyterlab 

taking care to do so in the desired conda environment, here sos.

Other Packages

You may also wish to install the rather impressive 3D animation package vpython,

conda install vpython -c vpython

If all goes well, you will have installed a rather complete set of amazing high quality absolutely free software packages on your system that are consistent with Python 3.

For some quick help on conda see

https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/20/conda/

If you still prefer to do everything by hand, follow the instructions at

https://www.scipy.org/install.html

and

https://jupyter.org/install

Download

It is a good idea to organize your computer-based projects in a systematic way. For example, in your home directory (usually the area identified by the environment variable $HOME), you may wish to create a directory (i.e., folder) called Projects

cd
mkdir Projects

In a terminal window dedicated to running the jupyter lab environment, do

cd
cd Projects
jupyter lab

This will run the notebook in your browser and block the terminal window, which you can then iconize.

In another terminal window, go to folder Projects

cd
cd Projects

and execute the command

git clone https://github.com/hbprosper/INFN-SOS

This should download the package INFN-SOS to your current directory.

Notebooks

The notebooks provide detailed background information and explanations.

folders	description
01_prob	probability exercises
02_stats	statistics exercises
03_ml	machine learning exercises to be added!